01-24-2018 , 06:47 PM
E-valuate
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E-valuate provides three simple calculation tools for anyone seeking to do an evaluation. It is designed for researchers or evaluators, or those seeking to commission an evaluation and need a quick, easy and reliable means of estimating the key variables for their study.
In an impact evaluation, a test is undertaken to see whether a change measured in an intervention group is greater or less than that in a comparison group, and whether the change is statistically significant. The intervention group undertakes a ‘treatment’ of some kind, which the researcher would like to test in terms of its effectiveness in changing a variable of interest. The comparison group does not receive the treatment and therefore serves as a control for whether the treatment has an impact. The selection of individuals into treatment and comparison groups could be random as with a randomised control trial (RCT), but they could be matched through other means, often known as quasi-experimental methods. The same calculations apply for these different sampling methods in terms of sample size, power and calculating the minimum detectable effect size.
The bigger a change is measured in the variable of interest, the more likely it is to be statistically significant for a given sample size. Other variables are also vital, such as the power of the evaluation, where a larger sample size would be needed for 90% statistical power than for 80% for example. In addition, researchers and evaluators need to consider whether clustering needs to be applied. E-valuate provides all the key variables required to make these calculations.
The key variables involved and statistical terms associated are explained in the instructions below. Follow the instructions alongside your calculation to ensure you determine the most robust and accurate results for your evaluation.
E-valuate provides three simple calculation tools for anyone seeking to do an evaluation. It is designed for researchers or evaluators, or those seeking to commission an evaluation and need a quick, easy and reliable means of estimating the key variables for their study.
In an impact evaluation, a test is undertaken to see whether a change measured in an intervention group is greater or less than that in a comparison group, and whether the change is statistically significant. The intervention group undertakes a ‘treatment’ of some kind, which the researcher would like to test in terms of its effectiveness in changing a variable of interest. The comparison group does not receive the treatment and therefore serves as a control for whether the treatment has an impact. The selection of individuals into treatment and comparison groups could be random as with a randomised control trial (RCT), but they could be matched through other means, often known as quasi-experimental methods. The same calculations apply for these different sampling methods in terms of sample size, power and calculating the minimum detectable effect size.
The bigger a change is measured in the variable of interest, the more likely it is to be statistically significant for a given sample size. Other variables are also vital, such as the power of the evaluation, where a larger sample size would be needed for 90% statistical power than for 80% for example. In addition, researchers and evaluators need to consider whether clustering needs to be applied. E-valuate provides all the key variables required to make these calculations.
The key variables involved and statistical terms associated are explained in the instructions below. Follow the instructions alongside your calculation to ensure you determine the most robust and accurate results for your evaluation.